让蛋白口袋动起来，生成更真实的药物分子。
Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Models
- 用扩散模型同时生成药物分子和动态口袋结构
- 在2.4万对蛋白结构数据上训练，捕捉真实构象变化
- 适合需要考虑蛋白柔性的新药设计场景
深度生成模型正快速推动基于结构的药物设计，有望生成能结合特定蛋白靶标的化合物。然而，现有方法多假设蛋白结合口袋为刚性结构，忽略蛋白质自身灵活性及配体结合引发的构象重排，限制了其在实际药物发现中的应用。本文提出Apo2Mol，一种基于扩散的3D分子生成框架，显式建模蛋白结合口袋的构象灵活性。为此，我们从蛋白质数据库（PDB）中构建了一个包含超过24,000对实验解析的无配体-有配体结构对的数据集，用于表征配体结合引起的蛋白结构变化。Apo2Mol采用全原子层级图结构扩散模型，从输入的无配体状态同步生成3D药物分子及其对应的有配体口袋构象。实证研究表明，Apo2Mol在生成高亲和力配体方面达到当前最优性能，并能准确捕捉真实的蛋白口袋构象变化。
原文摘要 · Abstract (English)
Deep generative models are rapidly advancing structure-based drug design, offering substantial promise for generating small molecule ligands that bind to specific protein targets. However, most current approaches assume a rigid protein binding pocket, neglecting the intrinsic flexibility of proteins and the conformational rearrangements induced by ligand binding, limiting their applicability in practical drug discovery. Here, we propose Apo2Mol, a diffusion-based generative framework for 3D molecule design that explicitly accounts for conformational flexibility in protein binding pockets. To support this, we curate a dataset of over 24,000 experimentally resolved apo-holo structure pairs from the Protein Data Bank, enabling the characterization of protein structure changes associated with ligand binding. Apo2Mol employs a full-atom hierarchical graph-based diffusion model that simultaneously generates 3D ligand molecules and their corresponding holo pocket conformations from input apo states. Empirical studies demonstrate that Apo2Mol can achieve state-of-the-art performance in generating high-affinity ligands and accurately capture realistic protein pocket conformational changes.
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